The proposed project is a theoretical study designed to maximize the utility of restriction fragment length polymorphisms (RFLP) in estimating genetic risk in humans. Over 200 RFLP's have now been cloned form human DNA, and over 20 diseases are linked to at least one such marker. The theory that has been published on the use of RFLP's in diagnosis has focused on the use of a single marker flanking a disease-causing locus, and it has been emphasized that tight linkage to the disease locus is essential. Recently procedures for estimating risk using multiple flanking markers have been devised based on two different approaches: Bayesian probability theory and pedigree likelihood analysis. Despite the different foundations for these risk estimators, in this proposal it is shown that for simple pedigrees the final estimates of risk are identical. An advantage of the Bayesian approach is that it is less computation-intensive, and for large pedigrees or multiple markers this may be critically important. One clear result of this work is that, by using multiple markers, the requirement of tight linkage is considerably relaxed, and hence the number of diseases amenable to prenatal risk estimation is increased. The proposed work is to perform an analysis of the effects of linkage disequilibrium, linkage interference, population structure and consanguinity on the utility of linked markers and on the merits of simultaneous estimation of linkage parameters by maxium likelihood. The utility of a set of markers includes, 1) the fraction of at-risk families for whom the markers provide some information, 2) the degree of certainty of the resulting risk estimates, and 3) the confidence in the risk estimate. The effects of linkage disequilibrium and population structure may be of critical importance in devising the optimal schemes for detecting the most clinically useful sets of RFLP's.